A Semi-Supervised Learning Framework for Classifying Colorectal Neoplasia Based on the NICE Classification

作者：Yu Wang, Haoxiang Ni, Jielu Zhou, Lihe Liu, Jiaxi Lin, Minyue Yin, Jingwen Gao, Shiqi Zhu, Qi Yin, Jinzhou Zhu, Rui Li · 发表于：Journal of Imaging Informatics in Medicine · 年份：2024 · DOI：10.1007/s10278-024-01123-9 · 被引用次数：11 · 研究领域：Radiomics and Machine Learning in Medical Imaging、AI in cancer detection、Colorectal Cancer Screening and Detection

Labelling medical images is an arduous and costly task that necessitates clinical expertise and large numbers of qualified images. Insufficient samples can lead to underfitting during training and poor performance of supervised learning models. In this study, we aim to develop a SimCLR-based semi-supervised learning framework to classify colorectal neoplasia based on the NICE classification. First, the proposed framework was trained under self-supervised learning using a large unlabelled dataset; subsequently, it was fine-tuned on a limited labelled dataset based on the NICE classification. The model was evaluated on an independent dataset and compared with models based on supervised transfer learning and endoscopists using accuracy, Matthew's correlation coefficient (MCC), and Cohen's kappa. Finally, Grad-CAM and t-SNE were applied to visualize the models' interpretations. A ResNet-backboned SimCLR model (accuracy of 0.908, MCC of 0.862, and Cohen's kappa of 0.896) outperformed supervised transfer learning-based models (means: 0.803, 0.698, and 0.742) and junior endoscopists (0.816, 0.724, and 0.863), while performing only slightly worse than senior endoscopists (0.916, 0.875, and 0.944). Moreover, t-SNE showed a better clustering of ternary samples through self-supervised learning in SimCLR than through supervised transfer learning. Compared with traditional supervised learning, semi-supervised learning enables deep learning models to achieve improved performance with limit...